RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus roots
Abstract This research work was carried out to explore the influence of extraction methods on the extraction of antioxidant from Asparagus racemosus roots. The microwave‐assisted extraction (MAE) method was more efficient as compared with continuous shaking extraction (CSE) method in terms of total...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-03-01
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| Series: | Food Bioengineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/fbe2.12040 |
| _version_ | 1797858410274750464 |
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| author | Akshay K. Singh Vivek Kumar Karunakar Singh Sumaiya Fatima |
| author_facet | Akshay K. Singh Vivek Kumar Karunakar Singh Sumaiya Fatima |
| author_sort | Akshay K. Singh |
| collection | DOAJ |
| description | Abstract This research work was carried out to explore the influence of extraction methods on the extraction of antioxidant from Asparagus racemosus roots. The microwave‐assisted extraction (MAE) method was more efficient as compared with continuous shaking extraction (CSE) method in terms of total phenolic content (TPC), total flavonoids content (TFC), 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and total antioxidant capacity (TAC) content in the ethanolic extract of A. racemosus roots. Box–Behnken experimental design of response surface methodology (RSM) was used to optimize MAE method for three process parameters viz. solvent concentration (SC: 40%–90% v/v), extraction time (ET: 10–30 min), and microwave power (MP: 360–720 W). Model fitness of the experimental data was determined by multiple regression analysis and analysis of variance. The optimum targeted parameters were obtained as 65% v/v SC, 23 min ET, and 560 W MP, and the corresponding responses TPC, TFC, DPPH, FRAP, and TAC were found to be 9.377 mg GAE/g, 177.03 mg QE/g, 65.70%, 193.35 mM Fe+2/g, and 496.97 mg AAE/g with a desirability of 0.95. Fuzzy modeling with Mamdani‐type fuzzy inference was also utilized to obtain the most optimal conditions for specified process parameters and responses under the set of rules for process parameters and responses. The estimated results of responses by both RSM and fuzzy model showed good agreement with the experimental values. |
| first_indexed | 2024-04-09T21:12:56Z |
| format | Article |
| id | doaj.art-5940f76c8d6a458fb873f2b12c3f34c9 |
| institution | Directory Open Access Journal |
| issn | 2770-2081 |
| language | English |
| last_indexed | 2024-04-09T21:12:56Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Food Bioengineering |
| spelling | doaj.art-5940f76c8d6a458fb873f2b12c3f34c92023-03-28T13:54:36ZengWileyFood Bioengineering2770-20812023-03-0121758610.1002/fbe2.12040RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus rootsAkshay K. Singh0Vivek Kumar1Karunakar Singh2Sumaiya Fatima3Department of Food Technology Harcourt Butler Technical University Kanpur Uttar Pradesh IndiaDepartment of Food Technology Harcourt Butler Technical University Kanpur Uttar Pradesh IndiaDepartment of Food Technology Harcourt Butler Technical University Kanpur Uttar Pradesh IndiaDepartment of Food Technology Harcourt Butler Technical University Kanpur Uttar Pradesh IndiaAbstract This research work was carried out to explore the influence of extraction methods on the extraction of antioxidant from Asparagus racemosus roots. The microwave‐assisted extraction (MAE) method was more efficient as compared with continuous shaking extraction (CSE) method in terms of total phenolic content (TPC), total flavonoids content (TFC), 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and total antioxidant capacity (TAC) content in the ethanolic extract of A. racemosus roots. Box–Behnken experimental design of response surface methodology (RSM) was used to optimize MAE method for three process parameters viz. solvent concentration (SC: 40%–90% v/v), extraction time (ET: 10–30 min), and microwave power (MP: 360–720 W). Model fitness of the experimental data was determined by multiple regression analysis and analysis of variance. The optimum targeted parameters were obtained as 65% v/v SC, 23 min ET, and 560 W MP, and the corresponding responses TPC, TFC, DPPH, FRAP, and TAC were found to be 9.377 mg GAE/g, 177.03 mg QE/g, 65.70%, 193.35 mM Fe+2/g, and 496.97 mg AAE/g with a desirability of 0.95. Fuzzy modeling with Mamdani‐type fuzzy inference was also utilized to obtain the most optimal conditions for specified process parameters and responses under the set of rules for process parameters and responses. The estimated results of responses by both RSM and fuzzy model showed good agreement with the experimental values.https://doi.org/10.1002/fbe2.12040antioxidantAsparagus racemosusfuzzy modelingoptimizationRSM |
| spellingShingle | Akshay K. Singh Vivek Kumar Karunakar Singh Sumaiya Fatima RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus roots Food Bioengineering antioxidant Asparagus racemosus fuzzy modeling optimization RSM |
| title | RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus roots |
| title_full | RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus roots |
| title_fullStr | RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus roots |
| title_full_unstemmed | RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus roots |
| title_short | RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus roots |
| title_sort | rsm and fuzzy modeling approach for optimization of extraction of antioxidant compounds from asparagus racemosus roots |
| topic | antioxidant Asparagus racemosus fuzzy modeling optimization RSM |
| url | https://doi.org/10.1002/fbe2.12040 |
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